Physical activity (PA) and body composition are important lifestyle factors that influence public health. Research suggests that DNA regions (CpG site locations) are differentially methylated in a physically active population. This meta-analysis aimed to identify CpG sites associated with various levels of PA and associated metabolic pathways. The meta-analysis followed PRISMA guidelines using PubMed, SportDISCUS, Embase, Scopus, Cochrane and Web of Science. Epigenomic analyses performed on DNA of participants with no underlying health conditions were included. Articles were screened using Rayyan AI and extracted CpG sites, and their location were confirmed using the EWAS catalogue. Six studies comprising 770 subjects were included in this meta-analysis. The meta-analysis was performed on clinical metrics extracted from the six studies and showed that BMI, blood pressure, insulin and glucose testing are significantly improved upon PA intervention. Amongst the included studies, a total of 257 CpG sites were differentially methylated in physically active participants, with 134 CpGs located in 92 genes associated with obesity-related pathways. The identified differentially methylated genes either belonged to the lipid metabolism or insulin signalling pathway. The genes which were differentially regulated in multiple tissue types and studies are JAZF1 (insulin signalling, and lipid and carbohydrate metabolism pathways) and NAV1 (mTOR signalling pathway). In conclusion, the current epigenomic meta-analysis showed that PA levels induce differential DNA methylation signatures on genes that affect metabolism. To understand the positive molecular effects of PA, further research on the above candidate genes needs to be conducted amongst various levels of a physically active population.

Chemotherapy resistance is a recurring challenge in cancer treatment, with specific bacteria impairing the effectiveness of certain chemotherapies. This study reviews three bacteria and their impact on chemotherapy drugs: Mycoplasma and gemcitabine, Fusobacterium nucleatum and oxaliplatin, bacterial β-glucuronase and irinotecan. Bacteria can have wide-ranging effects on cancer treatment; for instance, they may affect drug metabolism, alter toxin conversion, and encourage cancer growth. Whilst the presence of these bacteria was found to have a detrimental effect on the efficacy of chemotherapy treatment, we also consider wider interactions and interdependencies of the microbiota with drug treatments. Some cancer therapies depend on the delicate balance of the microbiome whilst simultaneously disrupting it by their very nature, particularly when antibiotics are introduced. Further research into the complex relationship between bacteria and the tumour micro-environment is needed. Treatments that focus on the immune-oncology microbiome axis or that explore genetic predisposition through the use of biomarkers could also support a more personalised approach.

Aim: The aim of this study was to correlate single-nucleotide polymorphisms (SNPs) in the FTO and MC4R genes with body composition (BC) in populations with various levels of physical activity, and to investigate associations of SREBF1 methylation with the level of physical activity (PA) and BC. Methods: Fifty-six participants aged 18–65 years old with no underlying medical conditions were included in the study and were classified into sedentary/light PA (SLPA), moderate PA (MPA) and vigorous PA (VPA) groups using the International PA questionnaire (IPAQ). Anthropometric measures such as age, gender, body mass index (BMI) and body fat percentage (BFP) were recorded at the time of recruitment. Venous blood samples were collected during participant recruitment and DNA was extracted. Genotyping assays were performed for SNPs in FTO (rs9939609) and MC4R (rs17782313) using Taqman^® RT qPCR and TaqMan Genotyper software 1.7.1. Methylation analysis assay for CpG sites in the SREBF1 gene was performed on 56 samples using PyroMark^® Q48 Autoprep (Qiagen, Venlo, The Netherlands). The results were statistically analysed to identify any associations between FTO/MC4R genotypes and the level of PA, and between SREBF1 methylation status and the level of PA. This is the first study to investigate links between PA and quantitative methylation of SREBF1. Results: According to IPAQ guidance, the 56 participants were classified into SLPA n = 14, MPA n = 11 and VPA n = 31. The correlation analysis revealed that the FTO rs9939609 ‘A’ risk allele had a significant negative association with BFP in the VPA group (p = 0.0387); the MC4R rs17782313 ‘C’ risk allele had a significant positive association with BMI in the VPA group (p = 0.0256). In the SREBF1 pyrosequencing analysis, higher levels of methylation were observed in the VPA group (p = 0.07). Conclusions: We concluded that SNPs associated with obesity identified in FTO rs9939609 and MC4R rs17782313 could help to predict the molecular effects of PA. A high frequency of FTO risk variants in the cohort was observed and the VPA group could help maintain a healthy BFP.